Elucidating the underlying components of metacognitive systematic bias in the human dorsolateral prefrontal cortex and inferior parietal cortex

Metacognitive systematic bias impairs human learning efficiency, which is characterized by the inconsistency between predicted and actual memory performance. However, the underlying mechanism of metacognitive systematic bias remains unclear in existing studies. In this study, we utilized judgments of learning task in human participants to compare the neural mechanism difference in metacognitive systematic bias. Participants encoded words in fMRI sessions that would be tested later. Immediately after encoding each item, participants predicted how likely they would remember it. Multivariate analyses on fMRI data demonstrated that working memory and uncertainty decisions are represented in patterns of neural activity in metacognitive systematic bias. The available information participants used led to overestimated bias and underestimated bias. Effective connectivity analyses further indicate that information about the metacognitive systematic bias is represented in the dorsolateral prefrontal cortex and inferior parietal cortex. Different neural patterns were found underlying overestimated bias and underestimated bias. Specifically, connectivity regions with the dorsolateral prefrontal cortex, anterior cingulate cortex, and supramarginal gyrus form overestimated bias, while less regional connectivity forms underestimated bias. These findings provide a mechanistic account for the construction of metacognitive systematic bias.


Participants
The sample size in the current study was roughly determined by following previous study using a similar task paradigm 26 .20 subjects participated in the experiments conducted in the current study.All participants were right-handed, had normal visual acuity or corrected visual acuity, and had no personal or family history of neurological or psychiatric disorders based on their self-report.This experiment was approved by the ethics committee of Northeast Normal University.The present study was in agreement with the Helsinki Declaration and approved by the ethics committee of the Northeast Normal University (Study No. 2022020).The participants signed an informed consent form before the experiment and were paid for completing the experiment and received a payment of 100 CNY once the experiment was completed.

Stimuli
The 126 abstract word pairs from Yu, Jiang, and Li (2020) were used, and each item is middle difficulty (0.3 to 0.7) through a memory recognition task 27 .Among them, 120 pairs of words were used for the formal experiment, and the remaining six pairs of abstract words were used for practice.

Procedure
In this study, we used an event-related design (Fig. 1). Figure 1 showed the details of the procedure.The formal scan consisted of 4 runs, with a short break given to the participants at the end of each run.The task took approximately 30 min to complete inside the scanner.In the scanner and each run, participants performed an encoding and immediate judgment of learning (JOLs) task.During the encoding and immediate JOLs stage, participants saw random jitters on the center of the screen ranging from 0 to 4000 ms, followed by the presentation of an abstract word pair (e.g., "合格-风景", written in Latin characters "qualified-scenery") to be learned for 4000 ms (total 16 word pairs).After encoding each pair, participants saw one word from the pair (the cue) on the screen and were asked to predict how likely they would remember the unseen target in the post-scan recognition task on a four-point scale, with 1 indicating "will be absolutely forgotten" and 4 indicating "will be absolutely remembered".Participants had 4000 ms to press a button to indicate their estimated performance, and responses were collected online using an MRI-compatible button box.After the encoding-JOLs, a distraction task outside the scanner was asked to complete for 3 min.Participants also were not in the scanner during the recognition-test phase.In a recognition test trial, participants saw a previous cue word that was studied at the top of the screen, and the target word and two distractor words appeared in random locations (left, center, or right) on the bottom.Participants were asked to indicate which of the three words on the bottom had been paired initially with the cue at the top in 3000 ms.Each trial was associated with a fixed-interval fixation of 500 ms.

fMRI data acquisition
Neuroimaging data were acquired on a UIH Prisma 3.0 T MRI scanner with a 64-channel head-neck coil.The participant was placed in a supine position with a sponge pad inside the coil to hold the head in place and was asked to keep the head and body still during the scanning process.The functional image was a 32-slice axial image, measured by a T1 -weighted echo-planar images (EPI) sequence, covering the entire cerebral cortex (main technical parameters: TR = 2000 ms, TE = 30 ms, Flip angle = 80°, FOV = 230 mm × 230 mm, Matrix size = 64 × 64, slice thickness = 3.5 mm, sequential acquisition = 32 axial slices, voxel size = 3.5 × 3.5 × 4.2 mm).Each functional scanning session contained 207 time points, with a total of 4 runs.Structural images were collected using a T1-weighted 3D MPRAGE sequence (TR = 7 ms, TE = 3 ms, Flip angle = 9°, FOV = 230 mm × 230 mm, Matrix size = 384 × 384, slice thickness = 1 mm, sequential acquisition = 160 axial slices, voxel size = 0.5 × 0.5 × 0.5 mm), in order to coregister with the functional images.

fMRI data preprocessing
Imaging analysis was performed using spm12 (http:// www.fil.ion.ucl.ac.uk/ spm) 28 .First, all the EPI DICOM data were converted to NIFTI format.The first three images from each run were automatically discarded by the scanner to allow scanner equilibrium.Second, all volume slice scan times were corrected to the middle time slice and realigned to the first scan to correct for head motion.Third, the structural images of each subject were coregistered with the mean functional images, and then the images were normalized to the Montreal Neurological Institute template.Fourth, all voxels were resampled to 3 × 3 × 3 mm.Last, all functional volumes were smoothed by using an 8-mm FWHM isotropic Gaussian kernel.

Behavioral data analysis
Using participants' responses on the post-scan recognition test, we sorted trials based on JOLs magnitude and recognition performance.At the JOLs stage, participants were required to make immediate JOLs using a 1-4 scale.The 1 and 2 indicate that the participant will forget, while 3 and 4 indicate that the participant will remember.The four-point scale was used to fit the fMRI environment and was based on previous fMRI studies 29 .In the post-scan recognition test, a correct recognition was recorded as 1, and a failed recognition or timeout was recorded as 0. Therefore, items were given either an R (will remember) or an F (will forget) estimation in the JOLs stage and were either subsequently remembered (r) or subsequently forgotten (f) in the post-scan recognition memory test.This study aimed to investigate metacognitive systematic bias by comparing overestimated bias to underestimated www.nature.com/scientificreports/bias.The classification of metacognitive systematic bias is of great importance.We classified metacognitive systematic bias into two types 14 : (1) JOLs magnitude was high ("will remember" prediction) but was later failed to recognize in the post-scan recognition test (JOL high M low ), which is overestimated bias.(2) JOLs magnitude was low ("will forget" prediction) but was later correctly recognized in the post-scan recognition test (JOL low M high ), which is underestimated bias.Regarding the classification of the 4-point scale data into two categories, this decision was a deliberate choice, tailored specifically to fulfill the research objectives of elucidating the neural mechanisms that underlie metacognitive biases.By organizing the data into two representative categories, the authors intended to pinpoint and contrast the neural disparities between the two types of metacognitive biases, ultimately disclosing their underlying formation mechanisms.The behavioral data analysis has three steps: First, we calculated response time (RT) and proportion between the JOL high M low and JOL low M high conditions to test the feasibility of further fMRI analysis.This step was to confirm that both overestimated bias and underestimated bias were not happening by chance.Second, metacognitive sensitivity was calculated for each participant to evaluate the overall metacognitive monitoring accuracy via meta-d/d values in accordance with Maniscalco and  Lau (2012).Then, metacognitive sensitivity was calculated for both JOL high M low and JOL low M high conditions and should be compared between the JOL high M low and JOL low M high conditions.This approach can provide evidence of which type of metacognitive systematic bias is more sensitive.Because metacognitive sensitivity is an index that measures the accuracy of JOLs 30 .We have known that metacognitive systematic bias has low JOL accuracy, but it remains unclear whether overestimated bias or underestimated bias has less accuracy.Third, the recognition task performance was measured to ensure the effectiveness of the materials and tasks used in the experiment.

Univariate analysis
The GLM method, as implemented in the SPM toolbox, was used to analyze the BOLD responses to metacognitive systematic bias.For all analysis, events were modeled at the time of the stimulus onset and convolved with the canonical hemodynamic response function (HRF) using a double-gamma function.These events were then superimposed for all trials to fit with the fMRI signals of each voxel.At the JOLs stage, the event was time-locked to the onset of the stimuli, with a duration that was the summation of the presentation period (4 s) and the same duration as the event.The GLM model was based on the JOLs task.The GLM model was based on JOLs task, we separated two task-related events, including JOLs magnitude was high ("will remember" prediction) but was later failed to recognize in post-scan recognition test (JOL high M low ) and JOLs magnitude was low ("will forget" prediction) but was later correct recognized in post-scan recognition test (JOL low M high ).Motion correction parameters were entered as covariates of no interest, along with a constant term per run.The regressors were convolved with a canonical hemodynamic response function.Low-frequency drifts were excluded with a 1/128 Hz high-pass filter.Missed trials were not modeled.We defined two contrasts: JOL high M low vs. JOL low M high (1 -1), JOL low M high vs.JOL high M low (− 1 1).Contrasts constructed at the single participant level were then input into a second-level group analysis using a random-effects model.At the group level, metacognitive systematic bias fMRI activation was first obtained by applying a parametric one-sample t-test, then a paired sample t-test was used to compare the activation between different metacognitive systematic bias (JOL high M low versus JOL low M high , and vice versa).All reported clusters survived a threshold with p < 0.05 after correcting for multiple comparisons using the false discovery rate (FDR) method and consisted of ten or more significant voxels.

Regions of interest (ROI) analysis
ROIs were defined from previous literature 11,12,14

Multivariate pattern analysis
Multivariate pattern analysis (MVPA) was performed in MATLAB using the CoSMoMVPA Toolbox (https:// www.cosmo mvpa.org/) 31 .According to research on the use of MVPA for decoding in the same field 12 , we classified runwise beta images from GLMs modeling JOL high M low and JOL low M high activity patterns in ROI and whole-brain searchlight analyses.ROI MVPA was performed on normalized, non smoothed images using the ROI spheres as masks.Previous work has shown that these preprocessing steps have minimal impact on linear discriminant analysis (LDA) classification accuracy while allowing meaningful comparison across subject-specific differences in anatomy, as in standard fMRI analyses 32,33 .A single accuracy value per subject, per condition, and per ROI was extracted and used for group analysis and statistical testing.Whole-brain searchlight analyses used 3 mm-radius spheres centered around a given voxel for all voxels on spatially realigned and slice-time corrected images from each subject to create whole-brain accuracy maps.The significance of the classification accuracies of all voxels was tested using a non-parametric random permutation test (n = 5000) and results were corrected for multiple comparisons using the false discovery rate (FDR) approach (the significance threshold was set at p < 0.05).
For group-level analyses, these individual searchlight maps were spatially normalized and smoothed using a Gaussian kernel (8 mm FWHM) and entered into one-sample t-tests against chance accuracy 34 .Whole-brain cluster inference was performed in the same manner as in univariate analysis.For metacognitive systematic bias classifications, we conducted independent leave-one-run-out cross-validations on JOL high M low activity patterns and JOL low M high activity patterns.Pattern vectors from three of the four runs in each condition were used to train an LDA to predict the same classes in the vectors from the left-out run.We compared the true labels of the www.nature.com/scientificreports/left-out run with the labels predicted by the model and iterated this process for the other run to calculate a mean cross-validated accuracy independently for each condition.

Effective connectivity analysis
Dynamic Causal Modeling (DCM) is an effective connectivity analysis method for making inferences about causal relationships between brain regions.In this study, DCM was performed in SPM12 to compare brain connectivity strength between JOL high M low and JOL low M high .Specifically, the volumes of interest (VOI) were defined based on brain regions that have significant activation in the univariate analysis and multivariate pattern analysis.In other words, only VOIs were significant in univariate analysis, and multivariate pattern analysis included DCM analysis.Within each VOI, we chose the radius of 8 mm as centers of spherical VOIs based on contrasts within a GLM: JOL high M low versus JOL low M high and JOL low M high versus JOL high M low .According to previous studies 35 , in DCM analysis, three parameters need to be determined: matrix A (internal parameter), matrix B (modulation parameter), and matrix C (driving input parameter).Matrix A represents the intrinsic coupling among brain regions in the absence of external perturbations, and in this study, matrix A represents the whole metacognitive systematic bias.Matrix B is the change in brain region caused by the experiment, i.e., the JOL high M low or JOL low M high in this study.Matrix C is the perturbation of brain activity due to external input.Our primary interest was to estimate the quantitative differences between JOL high M low and JOL low M high in connectivity strength.Therefore, we focused on quantitative comparisons of the DCM parameters (in particular, matrix B) between JOL high M low and JOL low M high .The full model described above was first estimated at the individual level to derive DCM parameters for hypothesis testing at the group level.Then, groups of multiple subjects were averaged using PEB (Parametric Empirical Bayes) and BMR (Bayesian Model Reduction) 35 .The posterior probability (P) > 0.95 was used to indicate the significance of the model.Pairwise tests were also performed between JOL high M low and JOL low M high conditions, with posterior probabilities (P) > 0.95 indicating the significance of each brain region.

Behavioral results
Paired sample t-tests revealed no significant difference in RT and proportion between JOL high M low (M RT = 1024.83;M proportion = 0.23) and JOL low M high (M RT = 1169.64;M proportion = 0.23), t (16) = − 1.75, p = 0.099, BF 10 = 0.876; t (16) = 0.039, p = 0.969, BF 10 = 0.249 (see Fig. 2), indicating suitable classification per systematic bias type for further fMRI analysis.Metacognitive sensitivity for each participant was measured via meta-d/d values in accordance with Signal Detection Theory 30 , indicating that participants had lower metacognitive sensitivity, M = -1.26± 0.23.Then metacognitive sensitivity of JOL high M low and JOL low M high were measured, and paired sample t-tests showed significant difference, t (16) = -4.30,p < 0.001, BF 10 = 63.11,means JOL high M low have lower metacognitive sensitivity than JOL low M high .The correct recognition rate for all subjects was 56.60% ± 18%, indicating that the subjects completed the task carefully.

Univariate analysis results
Metacognitive systematic bias whole-brain responses were first analyzed using the conventional GLM method.As shown in Fig. 3A,B, JOL high M low , and JOL low M high all activated left dlPFC and left dmPFC.Other regions activated included left supramarginal, right precuneus, right superior frontal gyrus (SFG), left middle temporal gyrus (MTG), and right superior temporal gyrus (STG) under JOL high M low condition.We found elevated activity in ACC and left insula under JOL low M high condition (see Fig. 3 and Table 1).Furthermore, comparing metacognitive systematic bias BOLD activation between JOL high M low and JOL low M high showed other regions activated included left inferior parietal lobule (IPL) and left middle cingulate cortex (MCC) in JOL high M low > JOL low M high contrast, left parahippocampal in JOL high M low < JOL low M high contrast (see Fig. 3C,D).
The ROI analysis results showed no significant difference between JOL high M low and JOL low M high in left dlPFC (M = 0.

Multivariate pattern analysis (MVPA) results
A series of MVPAs were performed to obtain activity patterns of metacognitive systematic bias when remembering abstract word pairs.If systematic bias is shared across JOL high M low and JOL low M high , then common regions would be found in these two kinds of metacognitive systematic bias.

ROI MVPA analysis results
We performed an LDA decoding analysis using as input vectors the runwise beta images pertaining to JOL high M low and JOL low M high trials obtained from a GLM (12 input vectors in total).For JOL high M low /JOL low M high classification, we used standard leave-one-out independent cross-validations for each condition (JOL high M low /JOL low M high ), and we performed one sample t-test for each ROI and each condition, then conducted paired t-test for JOL high M low versus JOL low M high to obtain which region decoding metacognitive systematic bias information.Mean accuracy in classifying JOL high M low and JOL low M high was significantly above chance level in all ROIs (one-sample t-tests Bonferroni corrected for multiple comparisons α = 0.05/4 = 0.0125), shown in Fig. 4. In details, the mean accuracy of JOL high M low in each ROI: left dlPFC, t(16) = 21.68,p < 0.001; left dmPFC, t( 16) = 20.37,p < 0.001; left vmPFC, t(16) = 4.06, p < 0.001; ACC, t(16) = 20.66,p < 0.001; and JOL low M high in each ROI: left dlPFC, t(16) = 9.76, p < 0.001; left dmPFC, t(16) = 7.29, p < 0.001; left vmPFC, t(16) = 5.12, p < 0.001; ACC, t(16) = 15.09,p < 0.001.In particular, paired t-test used to analyze the common regions in ROI analysis showed JOL high M low classification accuracy was significantly different from JOL low M high in left dlPFC (t(16) = 21.68,p < 0.001), left dmPFC (t(16) = 14.46, p < 0.001), left vmPFC (t(16) = 5.12, p < 0.001), ACC (t(16) = 15.09,p < 0.001) (see Fig. 4C).Consistent with our hypothesis, JOL high M low and JOL low M high representations could be decoded in parts of the PFC and temporal cortex.

Effective connectivity results
Figure 5A,B shows the PEB analysis results for the modulatory effects on the effective connectivity between the modeled nodes.Connection strengths of the parameters whose posterior probability was higher than 0.95 (P > 0.95) are reported.The results under JOL high M low > JOL low M high condition found a significant single connection from left dlPFC to right precuneus, and bidirectional connections between left dmPFC and ACC, right precuneus and ACC, left dmPFC and left supramarginal gyrus, left insula and left supramarginal gyrus.
The results under the JOL low M high > JOL high M low condition showed a significant single connection from left dlPFC to left dmPFC, left insula to ACC, and bidirectional connections between left dmPFC and left supramarginal gyrus (Fig. 5B).

Discussion
A critical question in metacognitive monitoring is why individuals are sometimes inclined to overestimate or underestimate their memory performances.The neural mechanism of metacognitive systematic bias for overestimate prediction versus underestimate prediction was examined in this study using fMRI, machine learning decoding, and effective connectivity.In particular, we direct our attention on whether metacognitive brain regions and working memory regions engage in the formation of systematic bias when making JOLs.We found dissociated neural mechanisms that supported overestimated bias and underestimated bias, and the results should deepen our understanding of the cognitive and neural mechanisms of metacognitive systematic bias and thus help to answer the question of how this bias occurs.

Neural correlates of metacognitive systematic bias
Our results could help clarify the role of dlPFC in JOLs and the working memory process.As a typical metacognitive monitoring region, the activation of dlPFC was found in previous studies 13,14,36,37 .One possible explanation posits that increased dlPFC activity reflects partial retrieval of the target word in working memory 14 , but this hypothesis is contradicted by the fact that dlPFC is more dorsal to the regions involved in semantic elaboration 29 .The debate on dlPFC was partly resolved through a TMS study.Rounis  evidence that dlPFC TMS decreases metacognitive accuracy 38 .Using the JOLs paradigm and MVPA analysis, this study found strong evidence that dlPFC represents metacognitive monitoring.Specifically, univariate fMRI analysis showed that the JOL stage evoked metacognitive monitoring-related BOLD activity in dlPFC, and MVPA revealed that the decoding accuracy in dlPFC was significantly above the chance level in the experiment.
It is suggested that dlPFC, as a metacognitive monitoring region, plays a fundamental role in the formation of metacognitive systematic bias.Another region was found in ACC, which is known for performance monitoring 11 , integration of detected conflicts 39 , and attentional control mechanisms 40 .It has been shown that the cingulate cortex plays a major role in detecting discrepancies between the intended and the actual outcome of an action 41 .The significant classification accuracy of the ACC in the context of predicting memory performance (JOLs) might reflect its engagement in general performance monitoring.This result was supported by previous univariate fMRI analysis, and this study observed the ACC through machine learning decoding that supports the basic function of the ACC in the formation of metacognitive systematic bias.
As has been mentioned previously, making metacognitive monitoring predictions requires retrieval of the target word in working memory 9,10,14 .This is because at that time, the slow memory traces are weak, and participants will overestimate or underestimate their memory performance.Some regions represent the storage of working memory, e.g., inferior parietal lobule (IPL), supramarginal gyrus (SMG), angular gyrus, thalamus, superior parietal lobule (SPL) [42][43][44] .These regions associated with working memory were found in the results of univariate analysis of the experiment.In particular, the decoding accuracy of SMG in MVPA results was significantly above the chance level, which suggested SMG as a region involved in metacognitive systematic bias.
Just as working memory retrieval is an inference in metacognitive monitoring studies 9,10 , partial evidence could support the former hypothesis if working memory representations were found in metacognitive monitoring.This study detected that the working memory representation (SMG) provides critical evidence that making metacognitive monitoring predictions requires information from working memory, giving certain neural mechanism evidence to the dual-memories hypothesis and memory strength hypothesis.Furthermore, SMG not only has a single function for memory monitoring but also works in tandem with other brain regions to predict memory.The working memory trace is a possibility to produce overestimate or underestimate bias.The cognitive and neural mechanisms of overestimate bias and underestimate bias will be discussed in the brain connectivity "Results" section.
Moreover, through searchlight analysis, we discovered an interesting finding: a significantly higher decoding accuracy for JO Lhigh M low compared to JOL low M high within the right precuneus.This area of the brain, the precuneus, has been recognized as integral to metacognition, as supported by correlational evidence derived from functional activity analyses.For example, previous research has demonstrated a connection between metacognitive performance related to memory decisions and the precuneus 12,45,46 .Furthermore, the precuneus plays a pivotal role in retrospective confidence ratings, exhibiting greater activation when individuals express low confidence 47 .These observations suggest that the activation level of the precuneus serves as an indicator of both high and low confidence ratings.Notably, the present study focused on prospective confidence ratings and discovered that JOL high M low decoded more information from the precuneus than JOL low M high , thereby indicating that the precuneus reflects varying levels of confidence.
Throughout various phases of memory, the precuneus exhibits distinct patterns of activity.Specifically, when individuals provide confidence ratings immediately after encoding (judgments of learning), a stronger activation pattern is observed in the precuneus for higher confidence levels, while a weaker pattern is evident for lower confidence.Conversely, when confidence ratings are made following memory testing (judgments of confidence), a greater degree of precuneus activation is associated with lower confidence levels.Not only does the current study reveal variations in precuneal activity during confidence ratings, but it also suggests that the precuneus serves as the neural foundation for metacognitive biases.Furthermore, it appears that the precuneus contributes differentially to two types of metacognitive biases.In particular, it seems to play a more significant role in overestimation biases compared to underestimation biases, resulting in stronger activation and, consequently, higher decoding accuracy.This result not only corroborates the hypothesis of the involvement of the precuneus in metacognition processes 48,49 , but also strengthens the view of a domain-specificity in the assessment of metacognition 12 .

The different cognitive mechanisms between overestimated bias and underestimated bias
Through the formation of overestimate prediction and underestimate prediction, we found that SMG played an important role.However, the behavioral evidence showed that overestimate bias (JOL high M low ) had lower metacognitive accuracy than underestimate bias (JOL low M high ), suggesting different cognitive mechanisms behind them.The effective connectivity analysis results provided a network interpretation of the metacognitive accuracy difference.It revealed that higher brain connectivity was observed between the working memory region (IPL, SMG) and uncertainty signals region (insula) in overestimated prediction.Conversely, elevated metacognitive monitoring connectivity was found in underestimate prediction.A possible explanation for the lower metacognitive accuracy in overestimate bias is that more information increases participants' confidence 50 .When making judgments of learning, individuals require more resources (e.g., working memory resources and metacognitive monitoring resources).A series of irrelevant information can interrupt an individual's metacognitive monitoring, leading to overestimated predictions due to inflated memory performance.Conversely, when individuals have limited information, the available resources guide them to make underestimated predictions.The brain connection focuses more on metacognitive monitoring regions, providing neural network evidence for underestimated biases.Previous studies have focused on the behavioral mechanism of metacognitive systematic bias [5][6][7]51 and the measurement of bias using the behavioral method. Howver, they lack direct evidence comparing overestimated bias and underestimated bias.This study provides clear neural evidence regarding the formation of overestimated and underestimated biases and interprets the cognitive mechanism from an information availability perspective.

Dissociable neural networks supporting metacognitive systematic bias
When people are overconfident or underconfident in their memory predictions, dissociable neural connectivity is observed.The effective connectivity results provide evidence that the dlPFC and dmPFC play a central role in metacognitive monitoring processes, as significant connectivity was observed between the dlPFC and SMG, dmPFC, and SMG, especially for overestimate bias.The function of the dlPFC and dmPFC should be discussed in detail.Metacognitive monitoring studies have shown that the dlPFC and dmPFC are key brain regions when making metacognitive monitoring judgments 13,14,29,36,37 , while executive function studies suggest that the dlPFC and dmPFC are involved in working memory processes 52,53 .Using the JOLs paradigm and MVPA analysis, we found that the dlPFC and dmPFC are correlated with metacognitive monitoring, and the SMG represents the working memory process, indicating different neural mechanisms between metacognitive monitoring and working memory.Moreover, connectivity between the PFC and parietal cortex has been implicated in metacognition and decision-making studies 13,36,54 .In studies of decision-making, the ACC, vmPFC, and insula have been found to reveal uncertainty in decision-making 55 .The connectivity between the ACC and vmPFC, as well as the ACC and insula, was found to indicate uncertain decision-making, particularly in cases of underestimated bias, across two experiments.These findings suggest that different neural substrates are involved when making overestimated or underestimated biases.It is proposed that multiple regions, including metacognitive monitoring, working memory, and uncertainty, contribute to the formation of overestimated bias, while the collaboration of uncertainty monitoring and decision-making-related brain connectivity leads to the development of underestimated bias.

Conclusion
It is concluded that the present study has found a remarkable dissociation between the neural processes that underlie overestimate bias and underestimate bias.The results of MVPA and effective connectivity analyses lend support to the hypothesis that working memory is engaged in metacognitive monitoring, and systematic bias relies on the available information one acquires during the learning process.The different patterns of brain connectivity observed between frontal and parietal regions suggest the formation of distinct metacognitive systematic biases.These findings should enhance our understanding of the neural basis of human metacognitive systematic bias.

Figure 1 .
Figure 1.Experiment paradigm.(A) The rapid event-related design was used to fit the encoding-JOLs phase better.The major procedure in fMRI contained encoding-JOLs phase.Recognition-test phase was outside the fMRI scanner.Also, the details of each typical trial were introduced.(B) The arrangement of scanning runs.There were four encoding-JOLs sessions in total.

Figure 2 .
Figure 2. Behavior results in experiment 1.The left panel showed the RT results between JOL high M low and JOL low M high .The right panel represents proportion results and metacognitive sensitivity results between JOL high M low and JOL low M high .**p < 0.01.

Figure 3 .
Figure 3. Univariate analysis of metacognitive systematic bias activity in experiment.(A) JOL high M low activates left dlPFC, left supramarginal, right precuneus, right SFG, left MTG, right STG. (B) JOL low M high activates left dlPFC, ACC, and left insula.(C) univariate BOLD activation in left IPL and left MCC showed a significant difference in JOL high M low > JOL low M high contrast.(D) univariate BOLD activation in the left parahippocampal on JOL high M low < JOL low M high contrast.p < 0.05 FDR correction.

Figure 4 .
Figure 4. MVPA results.(A) Pattern vectors of two classes (e.g., JOL high M low and JOL low M high ) were used to train a decoder in a leave-one-run-out design that was then tested in the left-out pair.The process was iterated four times to test pairs from every run.(B) Mask used in ROI MVPA analysis (C) ROI results for JOL high M low versus JOL low M high classification accuracy in experiment.(D) Searchlight analysis results for JOL high M low classification accuracy in experiment.(E) Searchlight analysis for JOL low M high classification accuracy in experiment.***p < 0.001.All clusters in D and E are significant at a cluster-based permutation test (p < 0.05), corrected for multiple comparisons at p FDR < 0.05.

Figure 5 .
Figure 5. Effective connectivity results in experiment.(A) Effective connectivity results for JOL high M low > JOL low M high .(B) Effective connectivity results for JOL low M high > JOL high M low .Posterior probability was higher than 0.95 (P > 0.95).
used to analyze the common regions in searchlight analysis and showed higher decoding accuracy for JOL high M low than JOL low M high in the right precuneus (t (16) = 2.73, p = 0.016).These results revealed that the different part of the brain region represents information about specific metacognitive systematic bias, and common regions of PFC shared information across JOL high M low and JOL low M high .

Table 1 .
et al. (2010) found causal MNI coordinates and corresponding Z scores for brain areas activated by JOL high M low and JOL high M low conditions.